Using Wavelet Neural Network for the Identification of a Building Structure from Experimental Data

A wavelet neural network-based identification approach is presented in this paper to dynamically modeling a building structure. By combining wavelet decomposition and artificial neural networks, wavelet neural networks (WNN) are used for solving chaotic signal processing. The theoretical basis and basic operations of WNNs are first briefly introduced. Then the feasibility of structural behavior modeling and the possibility of structural health monitoring using WNNs are investigated and discussed. A practical application of WNNs to the structural dynamic modeling of a building frame in shaking tests is presented in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation.